Geography of organisations

A coherence in open source development and economic strength is visible, whereby the stronger the economy the higher the number of open source communities. Analysis of the geographic distribution of organisations behind OSS for sustainability projects shows an overwhelming majority (64%) place in Europe and North America. 28% of the projects are considered global as no geographical affiliation could be identified.

continent_his = df_organizations["continent"].value_counts().to_frame().rename_axis("continent_name")
continent_his.rename(index={"EU": "Europe", "NA": "North America", "": "Global", "OC":"Oceania", "AS":"Asia", "SA":"South America", "AF":"Africa"},inplace=True)
fig = px.pie(continent_his.reset_index(), values="continent", names="continent_name", color_discrete_sequence=color_discrete_sequence, hole=0.2)

fig.update_layout(title="Distribution of Organizations between Continents", font_size=16, showlegend=False, hovermode=False)
fig.update_traces(textposition='outside', textinfo='label+percent', marker=dict(line=dict(color='#000000', width=2)))
fig['layout'].update(margin=dict(l=0,r=0,b=0,t=40))
fig.show()

However, if one compares the ratios with other statistics, clear differences become apparent. We use baseline data from “The State of the Octoverse”, a study which provides the Geographic distribution of millions active GitHub users.

# similar pooling to the one in cell 53 could be done here for Africa + Oceania

fig = px.pie(df_users_continent_cotoverse, values=0, names="index", color_discrete_sequence=color_discrete_sequence, hole=0.2)

fig.update_layout(title="Distribution of all GitHub Users between Continents", font_size=16, showlegend=False, hovermode=False)
fig.update_traces(textposition='outside', textinfo='label+percent', marker=dict(line=dict(color='#000000', width=2)))
fig['layout'].update(margin=dict(l=0,r=0,b=0,t=40))
fig.show()

At the country level , the United States, Germany, France, and the United Kingdom stand out. Also, despite having more GitHub users than Europe overall, Asia accounts for only 1.9% of organisations working in OSS for sustainability. What’s more, the absence of Indian communities is noticeable despite the large number of open source developers present, no large organisations or projects could be identified. Despite the high number of scientific publications in general, there are very few organisations and projects from China.

df_countries = (
    df_organizations["ISO_3"]
    .value_counts()
    .to_frame()
    .rename_axis("country")
    .reset_index()
)
df_countries = df_countries.rename(columns={"ISO_3": "counts"})

fig = px.choropleth(
    df_countries,
    locations="country",
    locationmode="ISO-3",
    color="counts",
    color_continuous_scale=color_continuous_scale
)

fig.update_layout(title="Global Distribution of Organisations",
                    coloraxis_colorbar=dict(
                    title="Organisations",
                    ),)

fig['layout'].update(margin=dict(l=0,r=0,b=0,t=40))

fig.show()